@inproceedings{f341dfbd555d47a8b0eb3c3c67fd5b60,
title = "Nash Tracking Controls of Multi-input Nonzero-Sum Game System with Reinforcement Learning",
abstract = "This paper addresses Nash optimal tracking controls of the unknown multi-player game system with a reinforcement learning scheme. We propose a single-layer neural network to approximate the unknown multi-player system, where the system can be accurately identified. It is then used to calculate the Nash tracking controls, which are designed by two parts: the steady-state control and the optimal feedback tracking control. The optimal feedback tracking controls are obtained by using the HJB equation with the reinforcement learning scheme. The convergences of the NN weights and the approximated optimal controls are analyzed. Finally, a simulation is provided to illustrate the effectiveness of the methods in this paper.",
keywords = "Multi-input system, Nash equilibrium, Neural networks, Nonzero-sum game, Reinforcement learning",
author = "Yongfeng Lv and Xuemei Ren and Linwei Li and Jing Na",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8483384",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "2765--2769",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "United States",
}